Descriptive Study One-Health Application for Women Using Decision Tree-Based Classifier

This study focuses on the development of an eHealth application system using open-access datasets
from UCI Machine Learning Repository. This attempts to predict the onset of diabetes and chronic
kidney diseases grounding from the generated predictive models. Decision models are created using
C4.5, ID3 and CART algorithms with RapidMiner data science platform. Models incurred the highest
assessment are the bases of the developed system following Agile Software Development Life Cycle
Model. Easy access to healthcare workers through teleconsultation, diabetes and chronic kidney
disease (CKD) online diagnosis, and maternal care videos are possible with this study. Based on the
respondents’ response, the strongest point of the system was its portability, which earned the highest
average mean among categories of system evaluation. Thus, the system addresses the shortcomings
of healthcare in terms of distance and timeliness of treatment fostering an equal access to healthcare.